Benchmark Almanac

COCO Captions

Gemma 3 27B 116.0 leads since March 2025
Dropped

Models write a caption for each image, and outputs are compared with five human captions per image using BLEU, METEOR, ROUGE, and CIDEr.

Model cards usually report CIDEr, which is not a percentage.

Made by
Microsoft COCO
Released
April 2015
Kind
Detection and captioning
Level
Everyday
Scored
Checked automatically
Answers
Open-ended
Tasks
Public, plus a private test set
Strongest model tested
Gemma 3 27B #164 on the capability index
Last in a launch post
March 2025, dropped before it saturated
Last model added
March 2025

In the authors’ words

In this paper we describe the Microsoft COCO Caption dataset and evaluation server. When completed, the dataset will contain over one and a half million captions describing over 330,000 images. For the training and validation images, five independent human generated captions will be provided. To ensure consistency in evaluation of automatic caption generation algorithms, an evaluation server is used. The evaluation server receives candidate captions and scores them using several popular metrics, including BLEU, METEOR, ROUGE and CIDEr. Instructions for using the evaluation server are provided.

Abstract of Microsoft COCO Captions: Data Collection and Evaluation Server (arXiv:1504.00325, CC0)
3
Models scored in lab cards
1
Launch post lists it, from 1 lab
3
System cards report it, from 1 lab

Gemma 3 27B leads with 116.0 among models released by March 2025

Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.

The newest model with a score here came out in March 2025, and nobody has reported a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:

  1. 1 Gemma 3 27B 116.0
  2. 2 Gemma 3 12B 111.0
  3. 3 Gemma 3 4B 102.0

Source: Labs’ model cards

#ModelScoreSettingDateSource
1 Gemma 3 27B 116.0 Mar 2025 Lab model card
2 Gemma 3 12B 111.0 Mar 2025 Lab model card
3 Gemma 3 4B 102.0 Mar 2025 Lab model card

Number one over time

Lab claims count from the day the card came out, independent runs from the model’s release.

Google DeepMind
Lab claims Not tested since Mar 2025
Gemma 3 27B 116.0

Source: Labs’ model cards

Scores over time

All the scores we have, one dot each, so a model tested at several settings appears more than once.

Reported by the lab in its model card
100.0 105.0 110.0 115.0 120.0 2025 100.0 105.0 110.0 115.0 120.0 2025

Source: Labs’ model cards

No launch post has listed it since March 2025

Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.

Google DeepMind
DateLaunch postScoreAs the post writes it
Mar 12, 2025 Gemma 3 27B 116 COCOcap (PT)

3 system cards report it, from 1 lab

Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.

DateDocumentScoreAs the document writes itSection
Mar 25, 2025 Gemma 3 4B technical report 102 COCO caption, CIDEr, PT, 4-shot, val split without P&S (Table 11) Capabilities
Mar 25, 2025 Gemma 3 27B technical report 116 COCO caption, CIDEr, PT, 4-shot, val split without P&S (Table 11) Capabilities
Mar 25, 2025 Gemma 3 12B technical report 111 COCO caption, CIDEr, PT, 4-shot, val split without P&S (Table 11) Capabilities

More in Vision

All 281 in Vision
  • MMMU In use 11.5K college-level multimodal questions across 30 subjects. Multiple-choice and open accuracy. GPT-5.1 85.4%
  • MMMU-Pro In use Harder MMMU: college-level multimodal questions with more options and vision-only inputs to remove text shortcuts. GPT-5.6 Sol 84.6%
  • CharXiv In use Chart understanding on real arXiv figures. Reasoning questions require synthesizing information from complex charts. Claude Fable 5 93.5%
  • MATH-Vision (MathVision) In use 3,040 competition math problems with visual context. Answer accuracy. Qwen 3.8 Max 97.7%
  • MMStar In use Models answer 1,500 human-curated image questions that cannot be solved without the image, across 6 core capabilities. Scored by accuracy. Qwen 3.8 Max 85.9%
  • ChartQA In use Models answer human-written and generated questions about chart images that need visual and arithmetic reasoning. Scored by relaxed accuracy with a 5 percent numeric tolerance. Command A Vision 90.9%